Reliable millimeter-wave frequency synthesis requires oscillator designs that combine wide tuning coverage, low phase noise, and stable operation over broad frequency spans. This work introduces a machine-learning-driven framework for the automated design of LC Digitally Controlled Oscillators (DCOs) covering the 5–30 GHz range in 22-nm FDSOI technology. Surrogate models based on gradient-boosted ensembles are trained to predict oscillation frequency and phase noise directly from device dimensions, tank parameters, and bias conditions, enabling efficient navigation of the multi-dimensional design space. A targeted data-augmentation strategy enhances model generalization throughout the entire operating spectrum, while a frequency-aware decomposition further improves accuracy across the full range of synthesized oscillators. The proposed synthesis algorithm translates user-defined specifications, such as center frequency, tuning range, and phase-noise limits, into feasible transistor-level parameter sets and reconstructs the corresponding tuning boundaries via calibrated tank-capacitance adjustment. The automatically generated designs exhibit strong agreement with schematic and post-layout simulations, achieving wide tuning coverage and competitive phase noise with minimal deviation from predicted values. The results demonstrate that data-driven modeling supports reproducible, scalable, and specification-centric DCO design, offering a systematic alternative to conventional manual procedures and significantly reducing design effort in millimeter-wave oscillator development.

Advanced noise simulation is performed using linear and periodic state RF-CMOS circuit vehicles. As a linear vehicle, an operation amplifier is designed with two amplification stages while as periodic state, a ring oscillator operating in the high frequency region. The small signal noise analyses and phase noise analyses are benchmarked versus large signal direct time domain noise analysis, in relation to the obtained accuracy, the simulation parameters ruling the accuracy and the needed simulation time. The theoretical background of direct time domain (transient) noise analysis, its implementation and the used simulation model together with simulation time-saving and circuit diagnostics capabilities are addressed. In addition, the respective MOSFET noise sources – thermal, flicker and gate noise – are analyzed per device, versus their contribution and their simulation accuracy for both cases (linear and periodic state). Simulation guidelines for a proper noise behavior extraction are summarized and categorized according to each circuit type.